Methods and systems for generating alternative content using adversarial networks implemented in an application programming interface layer

ABSTRACT

Methods and systems for using a generative adversarial network to generate personalized content in real-time as a user accesses original content. The methods and systems perform the generation through the use of an application programming interface (“API”) layer. Using the API layer, the methods and systems may generate alternative content as a user accesses original content (e.g., a website, video, document, etc.). Upon receiving this original content, the API layer access the generative adversarial network to create personalized alternative content.

FIELD OF THE INVENTION

The invention relates to generating alternative content using generativeadversarial networks implemented in an application programming interfacelayer.

BACKGROUND

In recent years, users are increasingly receiving content on numerousdifferent platforms. Moreover, users are increasingly accessing thiscontent through different channels. However, these increases in bothavailable content and its accessibility creates issues for generatingpersonalized content for users.

SUMMARY

Methods and systems are described herein for generating personalizedcontent. For example, users increasingly steer themselves towards morepersonalized content and content providers are increasingly attemptingto personalize content for users. Conventional methods of generatingpersonalized content relied on tracking users and then providing onepre-generated piece of content from a plurality of pre-generated piecesof content. However, as the amount of content increases, and usersroutinely access the content using different devices, the pre-generationof so many variations of content becomes attenable. To overcome thisproblem, the methods and systems describe a way to generate alternativecontent that is personalized to a user in real time.

Specifically, the methods and systems use a generative adversarialnetwork to generate personalized content in real time as a user accessesoriginal content. The methods and systems perform the generation throughthe use of an application programming interface (“API”) layer. Using theAPI layer, the methods and systems may generate alternative content as auser accesses original content (e.g., a website, video, document, etc.).Upon receiving this original content, the API layer accesses thegenerative adversarial network to create personalized alternativecontent. To provide this approach, the methods and systems overcomeseveral technical hurdles. First, in order to limit the amount oforiginal content that is required to be processed, the methods andsystems first break the content into sections. The methods and systemsthen determine which section of content is likely to be of interest tothe user. Upon determining the section of interest, the methods andsystems create a content map of the section and identify the key contentcharacteristics (e.g., keywords, phrases, objects, images, etc.) thatare the basis for alternative content. Using the content characteristicsand metadata describing these characteristics, the methods and systemscreate a feature input for the generative adversarial network. Thegenerative adversarial network creates alternative contentcharacteristics, which may be included into the content mapping. Bybasing the feature input on the content characteristics and metadatadescribing these characteristics for only the section of content andthen repopulating the content map for the section, the amount ofprocessing required for the generative adversarial network is reduced(e.g., allowing for real-time generation of alternative content).

Second, in many instances, alternative content may include a mix of bothtextual and image data (and/or other types of data). However, thismixture may create issues for the generative adversarial network.Accordingly, the methods and systems use specialized architectures andtraining strategies that allow for text to image synthesis and viceversa. For example, the system may train on a subset of trainingcategories (e.g., subsets within various categories of data), whereinthe subsets are linked to specific characteristics (e.g., section and/orcontent characteristics). The system may include a generator anddiscriminator that compete in a two player minimax game for each of thesubsets of training categories. The system may then combine the resultsof this training to create overall alternative content. Finally, thesystem may perform a final verification that the initial sectioncharacteristics (e.g., the characteristic that made the section ofinterest to the user) is preserved. Upon verification, the system maygenerate the alternative content for the user.

In some aspects, methods and systems for generating alternative contentusing generative adversarial networks implemented in an applicationprogramming interface layer are described. For example, the system mayreceive content for display, in a user interface of a user device, to auser, wherein the content includes a plurality of sections. The systemmay identify a section of the plurality of sections as having a sectioncharacteristic, wherein the section characteristic is indicative of thesection being of interest to the user. The system may parse the sectionfor a content characteristic and metadata describing the contentcharacteristic. The system may generate a content map for the sectionbased on the parsing, wherein the content map indicates a position ofthe content characteristic in the section. The system may generate afeature input based on the content map and the metadata. The system mayinput the feature input into a generative adversarial network, whereinthe generative adversarial network is trained to generate an output ofan alternative section, wherein the alternative section corresponds tothe content map and has an alternative content characteristic at theposition. The system may generate for display, in the user interface ofthe user device, the alternative section, wherein the alternativesection replaces the section in the plurality of sections of thecontent.

Various other aspects, features, and advantages of the invention will beapparent through the detailed description of the invention and thedrawings attached hereto. It is also to be understood that both theforegoing general description and the following detailed description areexamples, and not restrictive of the scope of the invention. As used inthe specification and in the claims, the singular forms of “a,” “an,”and “the” include plural referents unless the context clearly dictatesotherwise. In addition, as used in the specification and the claims, theterm “or” means “and/or” unless the context clearly dictates otherwise.Additionally, as used in the specification “a portion,” refers to a partof, or the entirety of (i.e., the entire portion), a given item (e.g.,data) unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-B show illustrative user interfaces displaying generatedalternative content, in accordance with one or more embodiments.

FIG. 2 shows an illustrative system diagram for generative adversarialnetworks for generating alternative content, in accordance with one ormore embodiments.

FIG. 3 is an illustrative system for generating alternative contentusing generative adversarial networks implemented in an applicationprogramming interface layer, in accordance with one or more embodiments.

FIG. 4 shows a flowchart of the steps involved in generating alternativecontent using generative adversarial networks implemented in anapplication programming interface layer, in accordance with one or moreembodiments.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention. It will beappreciated, however, by those having skill in the art, that theembodiments of the invention may be practiced without these specificdetails or with an equivalent arrangement. In other cases, well-knownstructures and devices are shown in block diagram form in order to avoidunnecessarily obscuring the embodiments of the invention.

FIGS. 1A-B show illustrative user interfaces displaying generatedalternative content, in accordance with one or more embodiments. Forexample, FIG. 1A shows a user interface (e.g., of a web browser)featuring original content (e.g., content published in its native formby a content provider). FIG. 1B shows a user interface (e.g., of a webbrowser) featuring alternative content (e.g., content published in itsnative form by the content provider with alternative section replacingand/or interwoven with the original content). For example, userinterface 100 may comprise content received for display, in a userinterface of a web browser on a user device, to a user.

As referred to herein, a “user interface” may comprise a human-computerinteraction and communication in a device, and may include displayscreens, keyboards, a mouse, and the appearance of a desktop. Forexample, a user interface may comprise a way a user interacts with anapplication or a website. As referred to herein, “content” should beunderstood to mean an electronically consumable user asset, such astelevision programming, as well as pay-per-view programs, on-demandprograms (as in video-on-demand (VOD) systems). Internet content (e.g.,streaming content, downloadable content, Webcasts, etc.), video clips,audio, content information, pictures, rotating images, documents,playlists, websites, articles, books, electronic books, blogs,advertisements, chat sessions, social media, applications, games, and/orany other media or multimedia and/or combination of the same. Asreferred to herein, the term “multimedia” should be understood to meancontent that utilizes at least two different content forms describedabove, for example, text, audio, images, video, or interactivity contentforms. Content may be recorded, played, displayed, or accessed by userequipment devices, but can also be part of a live performance.

In some embodiments, alternative content may be personalized for a userbased on the original content and user preferences (e.g., as stored in auser profile). A user profile may be a directory of stored usersettings, preferences, and information for the related user account. Forexample, a user profile may have the settings for the user's installedprograms and operating system. In some embodiments, the user profile maybe a visual display of personal data associated with a specific user, ora customized desktop environment. In some embodiments, the user profilemay be digital representation of a person's identity. The data in theuser profile may be generated based on the system actively or passivelymonitoring.

FIG. 1A shows user interface 100. User interface 100 includes contenthaving a plurality of sections. As referred to herein, a “section” maycomprise any of the more or less distinct parts into which something thecontent may be divided or from which the content is made up. Forexample, a section may be distinguished from another section by one ormore section characteristics. In user interface 100, the system mayidentify a section of the plurality of sections as having a sectioncharacteristic.

A section characteristic may comprise any characteristic thatdistinguishes one section from another. For example, a sectioncharacteristic may be media-related information (e.g., ordering, headinginformation, titles, descriptions, ratings information (e.g., parentalcontrol ratings, critic's ratings, etc.), source code data (e.g., HTML,source code headers, etc.), genre or category information, subjectmatter information, author/actor information, logo data, or otheridentifiers for the content provider), media format, file type, objecttype, objects appearing in the content (e.g., product placements,advertisements, keywords, context), or any other suitable informationused to distinguish one section from another. In some embodiments, thesection characteristic may also be human-readable text. The sectioncharacteristic may be determined to be indicative of the section beingof interest to the user based on a comparison of the sectioncharacteristic and user profile data for the user.

For example, user interface 100 may include section 102. The system mayidentify section 102 based on a paragraph, section break, and/or an HTMLtag. The system may parse the section for a content characteristic(e.g., content characteristic) and metadata describing the contentcharacteristic, wherein the metadata indicates a context of the contentcharacteristic, and wherein the content characteristic compriseshuman-readable text. For example, as shown in user interface 100, thesystem may identify content characteristic 104. As referred to herein, a“content characteristic” may comprise any of the more or less distinctparts into which something the section may be divided or from which thesection is made up. For example, a content characteristic may beanything that may distinguish one content characteristic from another.In some embodiments, content characteristic may be human-readable text.For example, the content characteristic may be a keyword, an image, anembedded object, etc.

The system may generate a content map for the section based on theparsing, wherein the content map indicates a position of the contentcharacteristic in the section. For example, the content map may includeeach content characteristic of a given section with the distances and/orpositions indicated. For example, the system may determine a CSSposition property for each characteristic. In another example, thesystem may use HTML absolute positioning to define a content map.

The system may then generate a feature input based on the content mapand the metadata, wherein the feature input comprises a vector array ofvalues indicative of the content map and the metadata. For example, thesystem may use a generative adversarial network, wherein the generativeadversarial network is trained to generate outputs of alternativesections, wherein the alternative sections correspond to content mapsand have alternative content characteristics at predetermined positions.

FIG. 1B shows user interface 150. User interface 150 includes contenthaving a plurality of sections similar to user interface 100. In userinterface 150, the system may replace a section from the originalcontent (e.g., section 102) with another section (e.g., alternativesection 106). For example, as described below, the system may replace asection of the original content with an alternative section. Forexample, the system may input the feature input into a generativeadversarial network, wherein the generative adversarial network istrained to generate an output of an alternative section (e.g.,alternative section 106), wherein the alternative section corresponds tothe content map and has an alternative content characteristic at theposition. For example, alternative section 106 may correspond to section102), but with alternative content characteristic 108 replacing contentcharacteristic 104. User interface 150 also shows additional alternativesection 110, which is a section not included in the original content.Alternative section 110 may be located at a point outside the originalcontent map, but the system may be anchored to alternative section 106.In some embodiments, the system may generate for display alternativesection 106 and alternative section 110 simultaneously.

FIG. 2 shows an illustrative system diagram for generative adversarialnetworks for generating alternative content, in accordance with one ormore embodiments. For example. FIG. 2 comprises system 200. System 200may be used to generate alternative content using generative adversarialnetworks implemented in an application programming interface layer.System 200, which may comprise a generative adversarial network, mayinclude various objects. For example, system 200 may include randominputs 202, which are fed into generator 204 to generate samples 206.Similarly, real data 208 may generate samples 210. Samples 206 andsamples 210 may be fed into discriminator 212. Outputs fromdiscriminator 212 may include discriminator loss 216 and generator loss214.

For example, in system 200 both generator 204 and discriminator 212 maybe neural networks. Generator 204 outputs may be connected directly toan input for discriminator 212. Through backpropagation, aclassification from discriminator 212 provides a signal that generator204 uses to update its weights. The back-propagation may comprisefine-tuning the weights system 200 (and/or generator 204 ordiscriminator 212) based on the error rate obtained in the previousepoch (i.e., iteration). Proper tuning of the weights allows system 200to reduce error rates.

For example, generator 204 may generate new data instances.Discriminator 212 discriminates between different kinds of datainstances. A generative adversarial network is a type of generativemodel. For example, given a set of data instances X and a set of labelsY, generator 204 may capture the joint probability p(X, Y), or just p(X)if there are no labels, whereas discriminator 212 captures theconditional probability p(Y|X).

Discriminator 212 may be a classifier that distinguishes real data(e.g., samples 210) from the data created by generator 204 (e.g.,samples 206). For example, discriminator 212 may use samples 210 aspositive examples during training. Discriminator 212 may use samples 210as negative examples during training. In system 200, discriminator 212connects to two loss functions (e.g., discriminator loss 216 andgenerator loss 214). During discriminator 212 training, discriminator212 ignores generator loss 214 and uses discriminator loss 216.

During discriminator 212 training, discriminator 212 classifies bothreal data and fake data from generator 204. Discriminator loss 216penalizes discriminator 212 for misclassifying a real instance (e.g.,samples 210) as fake or a fake instance (e.g., samples 206) as real.Discriminator 212 updates its weights through backpropagation fromdiscriminator loss 216 through the discriminator network. Generator 204of system 200 learns to create fake data by incorporating feedback fromdiscriminator 212 (e.g., it learns to make discriminator 212 classifyits output as real). In some embodiments, generator 204 trainingrequires tighter integration between generator 204 and discriminator 212than discriminator training requires. For example, system 200 trainsgenerator 204 using random inputs 202.

As generator 204 improves with training, discriminator 212 performancegets worse because discriminator 212 cannot easily tell a differencebetween samples 210 and samples 206. If generator 204 succeeds, thendiscriminator 212 may have a 50% accuracy. Accordingly, generator 204attempts to maximize generator loss 214.

System 200 provides significant advantages over conventional machinelearning. Specifically, system may process both text and image data.First, system 200 includes architecture and training strategy thatenables compelling text to image synthesis. For example, system 200 maytrain on a subset of training categories (e.g., subsets within samples206 and 210), wherein the subsets are linked to specific characteristics(e.g., section and/or content). For example, system 200 consists ofgenerator 204 and discriminator 212 that compete in a two player minimaxgame for each subset of training categories. For example, for eachsubset discriminator 212 tries to distinguish real training data foreach subset (e.g., samples 210) from synthetic data for each subset(e.g., samples 206), and generator 204 tries to fool discriminator 212.For example, system 200 may include text encoders/decoders and imageencoders/decoders for each subset.

System 200 may be trained subset features encoded by a hybridcharacter-level convolutional recurrent neural network. Both generator204 and discriminator 212 perform feed-forward inference conditioned onthe subset feature. In system 200, discriminator 212 observes two kindsof inputs: real images with matching text, and synthetic images witharbitrary text. System 200 implicitly separates two sources of error:unrealistic images (for any text), and realistic images of the wrongclass that mismatch the conditioning information. System 200 separatesthese error sources. For example, in addition to the real/fake inputs todiscriminator 212 during training, system 200 adds a third type of inputconsisting of real images with mismatched text, which discriminator 212learns to score as fake. By learning to optimize image/text matching inaddition to the image realism, discriminator 212 provides an additionalsignal to the generator.

It should be noted that additionally or alternatively, the system mayuse variational autoencoders to generate content. For example, avariational autoencoder provides a probabilistic manner for describingan observation in latent space. As such, instead of using an encoderwhich outputs a single value to describe each latent state attribute,the system may determine a probability distribution for each latentattribute. To describe the observation in a probabilistic manner, thesystem determines a probability distribution for each latent attribute.During decoding from the latent state, the system randomly samples fromeach latent state distribution to generate a vector as input for thedecoder. Accordingly, the variational autoencoder provides a model thatoutputs a range of possible values (e.g., a statistical distribution).These values can then be randomly sampled by the decoder. This creates acontinuous, smooth latent space representation in which values that arenearby to one another in latent space create similar reconstructions.

FIG. 3 is an illustrative system for generating alternative contentusing generative adversarial networks implemented in an applicationprogramming interface layer, in accordance with one or more embodiments.For example, system 300 may represent the components used for generatingalternative content based on initial content, as shown in FIGS. 1A-1Band using a generative adversarial network (as shown in FIG. 2). Asshown in FIG. 3, system 300 may include mobile device 322 and userterminal 324. While shown as a smartphone and personal computer,respectively, in FIG. 3, it should be noted that mobile device 322 anduser terminal 324 may be any computing device, including, but notlimited to, a laptop computer, a tablet computer, a hand-held computer,other computer equipment (e.g., a server), including “smart,” wireless,wearable, and/or mobile devices. FIG. 3 also includes cloud components310. Cloud components 310 may alternatively be any computing device asdescribed above and may include any type of mobile terminal, fixedterminal, or other device. For example, cloud components 310 may beimplemented as a cloud computing system and may feature one or morecomponent devices. It should also be noted that system 300 is notlimited to three devices. Users, may, for instance, utilize one or moredevices to interact with one another, one or more servers, or othercomponents of system 300. It should be noted, that, while one or moreoperations are described herein as being performed by particularcomponents of system 300, those operations may, in some embodiments, beperformed by other components of system 300. As an example, while one ormore operations are described herein as being performed by components ofmobile device 322, those operations, may, in some embodiments, beperformed by components of cloud components 310. In some embodiments,the various computers and systems described herein may include one ormore computing devices that are programmed to perform the describedfunctions. Additionally. or alternatively, multiple users may interactwith system 300 and/or one or more components of system 300. Forexample, in one embodiment, a first user and a second user may interactwith system 300 using two different components.

With respect to the components of mobile device 322, user terminal 324,and cloud components 310, each of these devices may receive content anddata via input/output (hereinafter “I/O”) paths. Each of these devicesmay also include processors and/or control circuitry to send and receivecommands, requests, and other suitable data using the I/O paths. Thecontrol circuitry may comprise any suitable processing, storage, and/orinput/output circuitry. Each of these devices may also include a userinput interface and/or user output interface (e.g., a display) for usein receiving and displaying data. For example, as shown in FIG. 3, bothmobile device 322 and user terminal 324 include a display upon which todisplay data (e.g., notifications).

Additionally, as mobile device 322 and user terminal 324 are shown astouchscreen smartphones, these displays also act as user inputinterfaces. It should be noted that in some embodiments, the devices mayhave neither user input interface nor displays and may instead receiveand display content using another device (e.g., a dedicated displaydevice such as a computer screen and/or a dedicated input device such asa remote control, mouse, voice input, etc.). Additionally, the devicesin system 300 may run an application (or another suitable program). Theapplication may cause the processors and/or control circuitry to performoperations related to generating alternative content.

Each of these devices may also include electronic storages. Theelectronic storages may include non-transitory storage media thatelectronically stores information. The electronic storage media of theelectronic storages may include one or both of (i) system storage thatis provided integrally (e.g., substantially non-removable) with serversor client devices, or (ii) removable storage that is removablyconnectable to the servers or client devices via, for example, a port(e.g., a USB port, a firewire port, etc.) or a drive (e.g., a diskdrive, etc.). The electronic storages may include one or more ofoptically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. The electronicstorages may include one or more virtual storage resources (e.g., cloudstorage, a virtual private network, and/or other virtual storageresources). The electronic storages may store software algorithms,information determined by the processors, information obtained fromservers, information obtained from client devices, or other informationthat enables the functionality as described herein.

FIG. 3 also includes communication paths 328, 330, and 332.Communication paths 328, 330, and 332 may include the Internet, a mobilephone network, a mobile voice or data network (e.g., a 5G or LTEnetwork), a cable network, a public switched telephone network, or othertypes of communications networks or combinations of communicationsnetworks. Communication paths 328, 330, and 332 may separately ortogether include one or more communications paths, such as a satellitepath, a fiber-optic path, a cable path, a path that supports Internetcommunications (e.g., IPTV), free-space connections (e.g., for broadcastor other wireless signals), or any other suitable wired or wirelesscommunications path or combination of such paths. The computing devicesmay include additional communication paths linking a plurality ofhardware, software, and/or firmware components operating together. Forexample, the computing devices may be implemented by a cloud ofcomputing platforms operating together as the computing devices.

Cloud components 310 may be a database configured to store user data fora user. For example, the database may include user data that the systemhas collected about the user through prior interactions, both activelyand passively. Alternatively, or additionally, the system may act as aclearing house for multiple sources of information about the user. Thisinformation may be compiled into a user profile. Cloud components 310may also include control circuitry configured to perform the variousoperations needed to generate alternative content. For example, thecloud components 310 may include cloud-based storage circuitryconfigured to generate alternative content. Cloud components 310 mayalso include cloud-based control circuitry configured to runs processesto determine alternative content. Cloud components 310 may also includecloud-based input/output circuitry configured to display alternativecontent.

Cloud components 310 may include model 302, which may be a machinelearning model (e.g., as described in FIG. 2). Model 302 may take inputs304 and provide outputs 306. The inputs may include multiple datasetssuch as a training dataset and a test dataset. Each of the plurality ofdatasets (e.g., inputs 304) may include data subsets related to userdata, original content, and/or alternative content. In some embodiments,outputs 306 may be fed back to model 302 as input to train model 302(e.g., alone or in conjunction with user indications of the accuracy ofoutputs 306, labels associated with the inputs, or with other referencefeedback information). For example, the system may receive a firstlabeled feature input, wherein the first labeled feature input islabeled with a known alternative content for the first labeled featureinput. The system may then train the first machine learning model toclassify the first labeled feature input with the known alternativecontent.

In another embodiment, model 302 may update its configurations (e.g.,weights, biases, or other parameters) based on the assessment of itsprediction (e.g., outputs 306) and reference feedback information (e.g.,user indication of accuracy, reference labels, or other information). Inanother embodiment, where model 302 is a neural network, connectionweights may be adjusted to reconcile differences between the neuralnetwork's prediction and reference feedback. In a further use case, oneor more neurons (or nodes) of the neural network may require that theirrespective errors are sent backward through the neural network tofacilitate the update process (e.g., backpropagation of error). Updatesto the connection weights may, for example, be reflective of themagnitude of error propagated backward after a forward pass has beencompleted. In this way, for example, the model 302 may be trained togenerate better predictions.

In some embodiments, model 302 may include an artificial neural network.In such embodiments, model 302 may include an input layer and one ormore hidden layers. Each neural unit of model 302 may be connected withmany other neural units of model 302. Such connections can be enforcingor inhibitory in their effect on the activation state of connectedneural units. In some embodiments, each individual neural unit may havea summation function that combines the values of all of its inputs. Insome embodiments, each connection (or the neural unit itself) may have athreshold function such that the signal must surpass it before itpropagates to other neural units. Model 302 may be self-learning andtrained, rather than explicitly programmed, and can performsignificantly better in certain areas of problem solving, as compared totraditional computer programs. During training, an output layer of model302 may correspond to a classification of model 302 and an input knownto correspond to that classification may be input into an input layer ofmodel 302 during training. During testing, an input without a knownclassification may be input into the input layer, and a determinedclassification may be output.

In some embodiments, model 302 may include multiple layers (e.g., wherea signal path traverses from front layers to back layers). In someembodiments, back propagation techniques may be utilized by model 302where forward stimulation is used to reset weights on the “front” neuralunits. In some embodiments, stimulation and inhibition for model 302 maybe more free-flowing, with connections interacting in a more chaotic andcomplex fashion. During testing, an output layer of model 302 mayindicate whether or not a given input corresponds to a classification ofmodel 302 (e.g., alternative content).

In some embodiments, model 302 may predict alternative content. Forexample, the system may determine that particular characteristics aremore likely to be indicative of a type of alternative content. In someembodiments, the model (e.g., model 302) may automatically performactions based on output 306. In some embodiments, the model (e.g., model302) may not perform any actions on a user's account. The output of themodel (e.g., model 302) is only used to decide which location and/or adelivery time offset to select.

System 300 also includes API layer 350. In some embodiments. API layer350 may be implemented on user device 322 or user terminal 324.Alternatively or additionally, API layer 350 may reside on one or moreof cloud components 310. API layer 350 (which may be A REST or Webservices API layer) may provide a decoupled interface to data and/orfunctionality of one or more applications. API layer 350 may provide acommon, language-agnostic way of interacting with an application. Webservices APIs offer a well-defined contract, called WSDL, that describesthe services in terms of its operations and the data types used toexchange information. REST APIs do not typically have this contract;instead, they are documented with client libraries for most commonlanguages including Ruby, Java, PHP, and JavaScript. SOAP Web serviceshave traditionally been adopted in the enterprise for publishinginternal services as well as for exchanging information with partners inB2B transactions.

API layer 350 may use various architectural arrangements. For example,system 300 may be partially based on API layer 350, such that there isstrong adoption of SOAP and RESTful Web-services, using resources likeService Repository and Developer Portal but with low governance,standardization, and separation of concerns. Alternatively, system 300may be fully based on API layer 350, such that separation of concernsbetween layers like API layer 350, services, and applications are inplace.

In some embodiments, the system architecture may use a microserviceapproach. Such systems may use two types of layers: Front-End Layer andBack-End Layer where microservices reside, in this kind of architecture,the role of the API layer 350 may provide integration between Front-Endand Back-End. In such cases, API layer 350 may use RESTful APIs(exposition to front-end or even communication between microservices).API layer 350 may use AMQP (e.g., Kafka, RabbitMQ, etc.). API layer 350may use incipient usage of new communications protocols such as gRPC.Thrift, etc.

In some embodiments, the system architecture may use an open APIapproach. In such cases, API layer 350 may use commercial or open sourceAPI Platforms and their modules. API layer 350 may use developer portal.API layer 350 may use strong security constraints applying WAF and DDoSprotection, and API layer 350 may use RESTful APIs as standard forexternal integration.

FIG. 4 shows a flowchart of the steps involved in transmitting digitalnotifications, in accordance with one or more embodiments. For example,process 400 may represent the steps taken by one or more devices asshown in FIGS. 1-3.

At step 402, process 400 (e.g., using one or more components in system300 (FIG. 3)) receives content for display. For example, the system mayreceive content for display, in a user interface of a user device, to auser, wherein the content includes a plurality of sections.

At step 404, process 400 (e.g., using one or more components in system300 (FIG. 3)) identifies a section of content for replacing. Forexample, the system may identify a section of the plurality of sectionsas having a section characteristic, wherein the section characteristicis indicative of the section being of interest to the user. In someembodiments, identifying the section of the plurality of sections ashaving the section characteristic may comprise identifying a pluralityof section characteristics in the section and comparing each of theplurality of section characteristics to user characteristics in a userprofile to determine a match. For example, the system may search eachsection for section characteristics (e.g., keywords, genre, authors,etc.) that may match preferences in a user profile. In some embodiments,when replacing a section, the system may switch specific words, objects,images, and/or rewrite the section.

At step 406, process 400 (e.g., using one or more components in system300 (FIG. 3)) parses the section. For example, the system may parse thesection for a content characteristic and metadata describing the contentcharacteristic. For example, in some embodiments, the contentcharacteristic may be an alphanumeric text string and the metadatadescribing the content characteristic that comprises a category of anobject corresponding to the alphanumeric text string. For example, themetadata may indicate a context of the content characteristic (e.g., themetadata may indicate the circumstances that form the setting for anevent, statement, or idea, and in terms of which it can be fullyunderstood and assessed, related to the content characteristic).

In some embodiments, parsing the section for the content characteristicmay comprise retrieving a list of content characteristics, comparingobjects in the section to the list of content characteristics, andidentifying the content characteristic based on matching an object ofthe objects to a listed content characteristic. For example, the list ofcontent characteristics may indicate content characteristics that may bereplaced. The system may search the section for these contentcharacteristics.

At step 408, process 400 (e.g., using one or more components in system300 (FIG. 3)) generates a content map. For example, the system maygenerate a content map for the section based on the parsing, wherein thecontent map indicates a position of the content characteristic in thesection.

At step 410, process 400 (e.g., using one or more components in system300 (FIG. 3)) generates a feature input. For example, the system maygenerate a feature input based on the content map and the metadata. Forexample, the feature input may comprise a vector array of valuesindicative of the content map and the metadata.

At step 412, process 400 (e.g., using one or more components in system300 (FIG. 3)) inputs the feature input into a generative adversarialnetwork to generate an output of an alternative section. For example,the system may input the feature input into a generative adversarialnetwork, wherein the generative adversarial network is trained togenerate an output of an alternative section, wherein the alternativesection corresponds to the content map and has an alternative contentcharacteristic at the position. In some embodiments, the contentcharacteristic may be textual data and the alternative contentcharacteristic may be different textual data. Alternatively, the contentcharacteristic may be textual data and the alternative contentcharacteristic may be image data, and wherein the generative adversarialnetwork is trained to translate the textual data into the image data.

In some embodiments, the generative adversarial network is furthertrained to generate an additional output of an additional alternativesection, wherein the additional alternative section corresponds to analternative position outside the content map, and wherein the additionalalternative section is simultaneously displayed with the alternativesection. For example, the system may generate additional alternativesections (e.g., section 110 (FIG. 1B)).

In some embodiments, the generative adversarial network may furthercomprise an autoregressive language model that performs natural languageprocessing using pre-trained language representations. For example,autoregressive language models use deep learning to produce human-liketext. For example, the autoregressive model may specify that an outputvariable depends linearly on its own previous values and on a stochasticterm (e.g., an imperfectly predictable term). The model is therefore inthe form of a stochastic difference equation (or recurrence relationwhich should not be confused with differential equation). Through theuse of this model, the system produces alternative text in a sectionthat is more human-like and preserves a natural tone and/or cadence.

In some embodiments, the system may parse the alternative section forthe section characteristic. For example, the system may perform anadditional check to ensure that the original section characteristicsthat were of likely interest to the user are still present in thealternative section. If not, the system may generate a new alternativesection. In response to identification of the section characteristic inthe alternative section, the alternative section is generated fordisplay.

At step 414, process 400 (e.g., using one or more components in system300 (FIG. 3)) generates for display the alternative section. Forexample, the system may generate for display, in the user interface ofthe user device, the alternative section, wherein the alternativesection replaces the section in the plurality of sections of thecontent.

It is contemplated that the steps or descriptions of FIG. 4 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 4 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order, in parallel,or simultaneously to reduce lag, or increase the speed of the system ormethod. Furthermore, it should be noted that any of the devices orequipment discussed in relation to FIGS. 1-3 could be used to performone of more of the steps in FIG. 4.

The above-described embodiments of the present disclosure are presentedfor purposes of illustration and not of limitation, and the presentdisclosure is limited only by the claims which follow. Furthermore, itshould be noted that the features and limitations described in any oneembodiment may be applied to any other embodiment herein, and flowchartsor examples relating to one embodiment may be combined with any otherembodiment in a suitable manner, done in different orders, or done inparallel. In addition, the systems and methods described herein may beperformed in real time. It should also be noted that the systems and/ormethods described above may be applied to, or used in accordance with,other systems and/or methods.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

1. A method, the method comprising: receiving content for display;replacing the content with alternative content generated using agenerative adversarial network; and generating for display, thealternative content.2. A method, the method comprising: receiving content for display, in auser interface of a user device, to a user, wherein the content includesa plurality of sections; identifying a section of the plurality ofsections as having a section characteristic, wherein the sectioncharacteristic is indicative of the section being of interest to theuser; parsing the section for a content characteristic and metadatadescribing the content characteristic; generating a content map for thesection based on the parsing, wherein the content map indicates aposition of the content characteristic in the section; generating afeature input based on the content map and the metadata; inputting thefeature input into a generative adversarial network, wherein thegenerative adversarial network is trained to generate an output of analternative section, wherein the alternative section corresponds to thecontent map and has an alternative content characteristic at theposition; and generating for display, in the user interface of the userdevice, the alternative section, wherein the alternative sectionreplaces the section in the plurality of sections of the content.3. The method of claim 2, wherein identifying the section of theplurality of sections as having the section characteristic furthercomprises: identifying a plurality of section characteristics in thesection; and comparing each of the plurality of section characteristicsto user characteristics in a user profile to determine a match.4. The method of claim 2, wherein the content characteristic is textualdata and the alternative content characteristic is different textualdata.5. The method of claim 2, wherein the content characteristic is textualdata and the alternative content characteristic is image data, andwherein the generative adversarial network is trained to translate thetextual data into the image data.6. The method of claim 2, wherein the content characteristic is analphanumeric text string and the metadata describing the contentcharacteristic comprises a category of an object corresponding to thealphanumeric text string.7. The method of claim 2, wherein the generative adversarial network isfurther trained to generate an additional output of an additionalalternative section, wherein the additional alternative sectioncorresponds an alternative position outside the content map, and whereinthe additional alternative section is simultaneously displayed with thealternative section.8 The method of claim 2, wherein the generative adversarial networkfurther comprises an autoregressive language model.9. The method of claim 2, further comprising parsing the alternativesection for the section characteristic, wherein the alternative sectionis generated for display in response to identification of the sectioncharacteristic in the alternative section.10. The method of claim 2, wherein the metadata indicates a context ofthe content characteristic.11. The method of claim 2, wherein parsing the section for the contentcharacteristic further comprises: retrieving a list of contentcharacteristics; comparing objects in the section to the list of contentcharacteristics; and identifying the content characteristic based onmatching an object of the objects to a listed content characteristic.12. A tangible, non-transitory, machine-readable medium storinginstructions that, when executed by a data processing apparatus, causethe data processing apparatus to perform operations comprising those ofany of embodiments 1-11.13. A system comprising: one or more processors; and memory storinginstructions that, when executed by the processors, cause the processorsto effectuate operations comprising those of any of embodiments 1-11.14. A system comprising means for performing any of embodiments 1-11.

1. (canceled)
 2. A method for generating alternative content usinggenerative adversarial networks implemented in an applicationprogramming interface layer, the method comprising: receiving contentfor display, in a user interface of a user device, to a user, whereinthe content includes a plurality of sections; identifying a section ofthe plurality of sections as having a section characteristic, whereinthe section characteristic is indicative of the section being ofinterest to the user; parsing the section for a content characteristicand metadata describing the content characteristic; generating a contentmap for the section based on the parsing, wherein the content mapindicates a webpage-based position of the content characteristic in thesection, the position being obtained via webpage-based positioninginformation associated with the content characteristic; generating afeature input based on the content map and the metadata; inputting thefeature input into a generative adversarial network, wherein thegenerative adversarial network is trained to generate an output of analternative section, wherein the alternative section corresponds to thecontent map and has an alternative content characteristic at thewebpage-based position; and generating for display, in the userinterface of the user device, the alternative section, wherein thealternative section replaces the section in the plurality of sections ofthe content.
 3. The method of claim 2, wherein identifying the sectionof the plurality of sections as having the section characteristicfurther comprises: identifying a plurality of section characteristics inthe section; and comparing each of the plurality of sectioncharacteristics to user characteristics in a user profile to determine amatch.
 4. The method of claim 2, wherein the content characteristic istextual data and the alternative content characteristic is differenttextual data.
 5. The method of claim 2, wherein the contentcharacteristic is textual data and the alternative contentcharacteristic is image data, and wherein the generative adversarialnetwork is trained to translate the textual data into the image data. 6.The method of claim 2, wherein the content characteristic is analphanumeric text string and the metadata describing the contentcharacteristic comprises a category of an object corresponding to thealphanumeric text string.
 7. The method of claim 2, wherein thegenerative adversarial network is further trained to generate anadditional output of an additional alternative section, wherein theadditional alternative section corresponds to an alternativewebpage-based position outside the content map, and wherein theadditional alternative section is simultaneously displayed with thealternative section.
 8. The method of claim 2, wherein the generativeadversarial network further comprises an autoregressive language modelthat performs natural language processing using pre-trained languagerepresentations.
 9. The method of claim 2, further comprising parsingthe alternative section for the section characteristic, wherein thealternative section is generated for display in response toidentification of the section characteristic in the alternative section.10. The method of claim 2, wherein the metadata indicates a context ofthe content characteristic.
 11. The method of claim 2, wherein parsingthe section for the content characteristic further comprises: retrievinga list of content characteristics; comparing objects in the section tothe list of content characteristics; and identifying the contentcharacteristic based on matching an object of the objects to a listedcontent characteristic.
 12. A non-transitory, computer-readable mediumfor generating alternative content using generative adversarial networksimplemented in an application programming interface layer, comprisinginstructions that, when executed by one or more processors, causeoperations comprising: receiving content for display, in a userinterface of a user device, wherein the content includes a plurality ofsections; identifying a section of the plurality of sections as having asection characteristic, wherein the section characteristic is indicativeof the section being of interest to the user; parsing the section for acontent characteristic and metadata describing the contentcharacteristic; generating a content map for the section based on theparsing, wherein the content map indicates a webpage-based position ofthe content characteristic in the section, the position being obtainedvia webpage-based positioning information associated with the contentcharacteristic; generating a feature input based on the content map andthe metadata; inputting the feature input into a generative adversarialnetwork, wherein the generative adversarial network is trained togenerate an output of an alternative section, wherein the alternativesection corresponds to the content map and has an alternative contentcharacteristic at the webpage-based position; and generating fordisplay, in the user interface of the user device, the alternativesection, wherein the alternative section replaces the section in theplurality of sections of the content.
 13. The non-transitory computerreadable medium of claim 12, wherein identifying the section of theplurality of sections as having the section characteristic furthercomprises: identifying a plurality of section characteristics in thesection; and comparing each of the plurality of section characteristicsto user characteristics in a user profile to determine a match.
 14. Thenon-transitory computer readable medium of claim 12, wherein the contentcharacteristic is textual data and the alternative contentcharacteristic is different textual data.
 15. The non-transitorycomputer readable medium of claim 12, wherein the content characteristicis textual data and the alternative content characteristic is imagedata, and wherein the generative adversarial network is trained totranslate the textual data into the image data.
 16. The non-transitorycomputer readable medium of claim 12, wherein the content characteristicis an alphanumeric text string and the metadata describing the contentcharacteristic comprises a category of an object corresponding to thealphanumeric text string.
 17. The non-transitory computer readablemedium of claim 12, wherein the generative adversarial network isfurther trained to generate an additional output of an additionalalternative section, wherein the additional alternative sectioncorresponds an alternative webpage-based position outside the contentmap, and wherein the additional alternative section is simultaneouslydisplayed with the alternative section.
 18. The non-transitory computerreadable medium of claim 12, wherein the generative adversarial networkfurther comprises an autoregressive language model that performs naturallanguage processing using pre-trained language representations.
 19. Thenon-transitory computer readable medium of claim 12, further comprisingparsing the alternative section for the section characteristic, whereinthe alternative section is generated for display in response toidentification of the section characteristic in the alternative section.20. The non-transitory computer readable medium of claim 12, whereinparsing the section for the content characteristic further comprises:retrieving a list of content characteristics; comparing objects in thesection to the list of content characteristics; and identifying thecontent characteristic based on matching an object of the objects to alisted content characteristic.
 21. A system for generating alternativecontent using generative adversarial networks implemented in anapplication programming interface layer, the system comprising: adatabase configured to store a machine learning model, wherein themachine learning model comprises: a generative adversarial network,wherein the generative adversarial network is trained to generateoutputs of alternative sections, wherein the alternative sectionscorrespond to content maps and have alternative content characteristicsat predetermined positions; and an autoregressive language model thatperforms natural language processing using pre-trained languagerepresentations; a computing device configured to: receive content fordisplay, in a user interface of a web browser on a user device, to auser, wherein the content includes a plurality of sections; identify asection of the plurality of sections as having a section characteristic,wherein the section characteristic is determined to be indicative of thesection being of interest to the user based on a comparison of thesection characteristic and user profile data for the user; parse thesection for a content characteristic and metadata describing the contentcharacteristic, wherein the metadata indicates a context of the contentcharacteristic, and wherein the content characteristic compriseshuman-readable text; generate a content map for the section based on theparsing, wherein the content map indicates a webpage-based position ofthe content characteristic in the section, the position being obtainedvia webpage-based positioning information associated with the contentcharacteristic; generate a feature input based on the content map andthe metadata, wherein the feature input comprises a vector array ofvalues indicative of the content map and the metadata; input the featureinput into the generative adversarial network, wherein the generativeadversarial network is trained to generate a first output of analternative section, wherein the alternative section corresponds to thecontent map and has an alternative content characteristic at thewebpage-based position; and the computing device configured to generatefor display, in the user interface of the web browser on the userdevice, the alternative section, wherein the alternative sectionreplaces the section in the plurality of sections of the content.